Tubulo-interstitial Nephritis and Uveitis (TINU) syndrome is a rare oculo-renal inflammatory disease. Renal tubular defects are usually found, but full proximal tubular abnormalities have rarely been described. We report the case of a 55-year old woman, native from Morocco, presenting with bilateral, non-granulomatous, anterior uveitis, mild renal insufficiency, leucocyturia and glycosuria. Further work-up showed hypophosphatemia and hyperphosphaturia, hypouricemia and hyperuricosuria, and hyper aminoaciduria, consistent with Fanconi syndrome. A kidney biopsy was obtained and showed diffuse interstitial infiltrates with tubular necrosis. The patient improved after the initiation of a corticosteroid therapy, with tapering dose. We reviewed the literature and found nine similar cases. This association mostly occurs in adult woman, without current evidence for an ethnic predilection, unlike previously reported. The renal prognosis seems favorable after corticosteroid therapy, even in case of severe renal injury. Nonetheless mild tubular defects may persist after treatment or spontaneous remission.

Background: During the COVID pandemic, research has shown an increase in candidemia cases following severe COVID infection and the identification of risk factors associated with candidemia. However, there is a lack of studies that specifically explore clinical outcomes and mortality rates related to candidemia after COVID infection.Objectives: The aim of this international study was to evaluate the clinical outcomes and identify factors influencing mortality in patients who developed candidemia during their COVID infection.Patients/Methods: This study included adult patients (18 years of age or older) admitted to the intensive care unit (ICU) and diagnosed with COVID‐associated candidemia (CAC). The research was conducted through ID‐IRI network and in collaboration with 34 medical centres across 18 countries retrospectively, spanning from the beginning of the COVID pandemic until December 2021.Results: A total of 293 patients diagnosed with CAC were included. The median age of the patients was 67, and 63% of them were male. The most common Candida species detected was C. albicans. The crude 30‐day mortality rate was recorded at 62.4%. The logistic regression analysis identified several factors significantly impacting mortality, including age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.02–1.07, p < .0005), SOFA score (OR 1.307, 95% CI 1.17–1.45, p < .0005), invasive mechanical ventilation (OR 7.95, 95% CI 1.44–43.83, p < .017) and duration of mechanical ventilation (OR 0.98, 95% CI 0.96–0.99, p < .020).Conclusions: By recognising these prognostic factors, medical professionals can customise their treatment approaches to offer more targeted care, leading to improved patient outcomes and higher survival rates for individuals with COVID‐associated candidemia.

Background/Objectives: Studies attempting to predict the development of severe respiratory failure in patients with a COVID-19 infection using machine learning algorithms have yielded different results due to differences in variable selection. We aimed to predict the development of severe respiratory failure, defined as the need for high-flow oxygen support, continuous positive airway pressure, or mechanical ventilation, in patients with COVID-19, using machine learning algorithms to identify the most important variables in achieving this prediction. Methods: This retrospective, cross-sectional study included COVID-19 patients with mild respiratory failure (mostly receiving oxygen through a mask or nasal cannula). We used XGBoost, support vector machines, multi-layer perceptron, k-nearest neighbor, random forests, decision trees, logistic regression, and naïve Bayes methods to accurately predict severe respiratory failure in these patients. Results: A total of 320 patients (62.1% male; average age, 54.67 ± 15.82 years) were included in this study. During the follow-ups of these cases, 114 patients (35.6%) required high-level oxygen support, 67 (20.9%) required intensive care unit admission, and 43 (13.4%) died. The machine learning algorithms with the highest accuracy values were XGBoost, support vector machines, k-nearest neighbor, logistic regression, and multi-layer perceptron (0.7395, 0.7395, 0.7291, 0.7187, and 0.75, respectively). The method that obtained the highest ROC-AUC value was logistic regression (ROC-AUC = 0.7274). The best predictors of severe respiratory failure were a low lymphocyte count, a high computed tomography score in the right and left upper lung zones, an elevated neutrophil count, a small decrease in CRP levels on the third day of admission, a high Charlson comorbidity index score, and a high serum procalcitonin level. Conclusions: The development of severe respiratory failure in patients with COVID-19 could be successfully predicted using machine learning methods, especially logistic regression, and the best predictors of severe respiratory failure were the lymphocyte count and the degree of upper lung zone involvement.

Pyomyositis is a primary infection of skeletal muscle that does not result from a contiguous disease, is hematogenous in origin, and often results in abscess formation. Although pyomyositis is classically an infection in tropical regions, it is an emerging infection in temperate climates. Pyomyositis is frequently caused by aerobic Gram-positive and Gram-negative bacteria.